BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Europe/Paris BEGIN:DAYLIGHT DTSTART:20260329T030000 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST END:DAYLIGHT BEGIN:STANDARD DTSTART:20261025T020000 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20261008T073202Z UID:A7E749AD-9ABB-4CF6-AB1E-928654B8ABF3 DTSTART;TZID=Europe/Paris:20261005T113000 DTEND;TZID=Europe/Paris:20261005T121500 DESCRIPTION:Artificial Intelligence (AI) has long been a subject of fascina tion\, oscillating between grand promises and inevitable disillusionment. While remarkable milestones\, like AI outperforming human champions in com plex games\, suggest we are entering a new era of computing\, a deeper loo k reveals that these breakthroughs come at a steep cost — demanding vast amounts of energy and intensive\, expensive training process. In areas li ke cognition\, decision-making\, and intelligence\, even our most advanced computing machines fall far short of the brain’s unparalleled efficienc y and compact design. The core of this challenge lies in the limitations o f conventional circuit elements and computing architectures\, which strugg le to replicate the brain’s complex\, nonlinear dynamics operating at th e edge of chaos. In this seminar\, I will introduce a new class of molecul ar circuit elements designed to capture the intricate\, reconfigurable log ic that mimics brain-like behaviour at the nanoscale. These devices can be operated as analog or digital elements\, or could be poised on the verge of instability\, offering a unique potential to emulate neural functions i n ways that traditional computing hardware cannot. Our journey explores th ese molecular systems from their foundational physics and chemistry\, all the way to integrated circuit design and on-chip applications [1-9] with t he aim of laying the groundwork for AI and machine learning platforms that can transcend the limitations of Moore's Law and lead to a new era of ene rgy-efficient computing.\n\nCo-sponsored by: INPACE\, SINANO\n\nSpeaker(s) : Sreetosh Goswami \, \n\nVirtual: https://events.vtools.ieee.org/m/579820 LOCATION:Virtual: https://events.vtools.ieee.org/m/579820 ORGANIZER:francis.balestra@grenoble-inp.fr SEQUENCE:31 SUMMARY:Webinar - Molecular Neuromorphic Building Blocks for Artificial Int elligence URL;VALUE=URI:https://events.vtools.ieee.org/m/579820 X-ALT-DESC:Description: <br /><p class="MsoNormal" style="text-align: justi fy\;"><span lang="EN-SG">Artificial Intelligence (AI) has long been a subj ect of fascination\, oscillating between grand promises and inevitable dis illusionment. While remarkable milestones\, like AI outperforming human ch ampions in complex games\, suggest we are entering a new era of computing\ , a deeper look reveals that these breakthroughs come at a steep cost &mda sh\; demanding vast amounts of energy and intensive\, expensive training p rocess. In areas like cognition\, decision-making\, and intelligence\, eve n our most advanced computing machines fall far short of the brain&rsquo\; s unparalleled efficiency and compact design. The core of this challenge l ies in the limitations of conventional circuit elements and computing arch itectures\, which struggle to replicate the brain&rsquo\;s complex\, nonli near dynamics operating at the edge of chaos. In this seminar\, I will int roduce a new class of molecular circuit elements designed to capture the i ntricate\, reconfigurable logic that mimics brain-like behaviour at the na noscale. These devices can be operated as analog or digital elements\, or could be poised on the verge of instability\, offering a unique potential to emulate neural functions in ways that traditional computing hardware ca nnot. Our journey explores these molecular systems from their foundational physics and chemistry\, all the way to integrated circuit design and on-c hip applications [1-9] with the aim of laying the groundwork for AI and ma chine learning platforms that can transcend the limitations of Moore's Law and lead to a new era of energy-efficient computing.</span></p> END:VEVENT END:VCALENDAR